# Python Library: `Chemical_Descriptors`
This function generates one of several fingerprint types from the list of **molecular fingerprints** available, each serving specific tasks in cheminformatics and computational chemistry.
### Importance of Fingerprint Types:
- **Distinct Representation:** Different fingerprint types capture various aspects of a molecule’s structure, allowing for versatile molecular comparisons.
- **Diverse Applications:** Depending on the task (such as similarity searching, classification, or clustering), choosing the right fingerprint type ensures better performance in chemical analysis and predictive modeling.
- **Accuracy in Modeling:** The right fingerprint type can significantly improve the accuracy of machine learning models and predictions based on molecular features.
### Number of Fingerprints:
## Functions
### **cal_rdkit_descriptor**(input_file, output_file, smiles_column)
**Description:**
This function calculates RDKit molecular descriptors for molecules specified in a CSV file (`input_file`) using SMILES strings from a specified column (`smiles_column`). The calculated descriptors are appended as additional columns to the original data and saved in a new CSV file named `<input_file_name>_rdkit_descriptor.csv`.
**Parameters:**
- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.
- `output_file` (str): Path where the output CSV file will be saved (optional if using the default naming convention).
- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.
**Output:**
The output will be saved as a CSV file named `<input_file_name>_rdkit_descriptor.csv`.
---
### **cal_lipinski_descriptors**(file_path, smiles_column, verbose=False)
**Description:**
This function calculates Lipinski descriptors for molecules specified in a CSV file (`file_path`) using SMILES strings from a specified column (`smiles_column`). It automatically saves the calculated descriptors to an output file named `<input_file_name>_lipinski_descriptors.csv`.
**Parameters:**
- `file_path` (str): Path to the input CSV file containing molecular data in SMILES format.
- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.
- `verbose` (bool, optional): If `True`, the function will print additional processing details. Default is `False`.
**Output:**
The output will be saved as a CSV file named `<input_file_name>_lipinski_descriptors.csv`.
---
### **cal_morgan_fpts**(input_file, smiles_column)
**Description:**
Calculates Morgan fingerprints for molecules specified in a CSV file (`input_file`) using SMILES strings from a specified column (`smiles_column`). The function saves the calculated fingerprints to an output file named `<input_file_name>_calculate_morgan_fpts.csv`.
**Parameters:**
- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.
- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.
**Output:**
The output will be saved as a CSV file named `<input_file_name>_calculate_morgan_fpts.csv`.
---
### **cal_mordred_descriptors**(input_file, smiles_column)
**Description:**
Computes Mordred descriptors for molecules specified in a CSV file (`input_file`) using SMILES strings from a specified column (`smiles_column`). The function saves the computed descriptors to an output file named `<input_file_name>_mordred_descriptors.csv`.
**Parameters:**
- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.
- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.
**Output:**
The output will be saved as a CSV file named `<input_file_name>_mordred_descriptors.csv`.
---
### **calculate_selected_fingerprints**(input_file, smiles_column)
**Description:**
Before using this function, execute the following code snippet to download and unzip the necessary files:
```bash
! wget https://github.com/dataprofessor/padel/raw/main/fingerprints_xml.zip
! unzip fingerprints_xml.zip
## Molecular Fingerprint Calculation
This function calculates 12 different types of molecular fingerprints:
- `AtomPairs2DCount`
- `AtomPairs2D`
- `EState`
- `CDKextended`
- `CDK`
- `CDKgraphonly`
- `KlekotaRothCount`
- `KlekotaRoth`
- `MACCS`
- `PubChem`
- `SubstructureCount`
- `Substructure`
Each enhanced dataset with fingerprints is saved as separate CSV files, appended with the respective fingerprint type name.
### Parameters:
- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.
- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.
### Output:
Each fingerprint type will be saved as a separate CSV file with the respective fingerprint type name appended.
For example: `<input_file_name>_AtomPairs2DCount.csv`.
---
## **fps**(filename, smiles_column, fp_type)
**Description:**
This function calculates a specified molecular fingerprint (`fp_type`) for each molecule in a CSV file. The user must provide:
- The CSV file (`filename`)
- The SMILES column name in the file (`smiles_column`)
- One of the following fingerprint types:
- `maccs`
- `avalon`
- `pattern`
- `layered`
- `map4`
- `secfp`
- `erg`
- `estate`
- `avalon-count`
- `ecfp`
- `fcfp`
- `topological`
- `atompair`
- `rdkit`
- `ecfp-count`
- `fcfp-count`
- `topological-count`
- `atompair-count`
- `rdkit-count`
### Parameters:
- `filename` (str): Path to the input CSV file containing molecular data in SMILES format.
- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.
- `fp_type` (str): The type of molecular fingerprint to calculate (choose from the list of fingerprint types above).
### Output:
The output will be saved as a CSV file named `<input_file_name>_<fp_type>.csv` depending on the fingerprint type chosen.
Ahmed Alhilal
=============
0.0.1 (05/01/2025)
-------------------
- First Release
Raw data
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"description": "# Python Library: `Chemical_Descriptors`\r\n\r\nThis function generates one of several fingerprint types from the list of **molecular fingerprints** available, each serving specific tasks in cheminformatics and computational chemistry.\r\n\r\n### Importance of Fingerprint Types:\r\n- **Distinct Representation:** Different fingerprint types capture various aspects of a molecule\u2019s structure, allowing for versatile molecular comparisons.\r\n\r\n- **Diverse Applications:** Depending on the task (such as similarity searching, classification, or clustering), choosing the right fingerprint type ensures better performance in chemical analysis and predictive modeling.\r\n\r\n- **Accuracy in Modeling:** The right fingerprint type can significantly improve the accuracy of machine learning models and predictions based on molecular features.\r\n\r\n\r\n### Number of Fingerprints:\r\n\r\n\r\n## Functions\r\n\r\n### **cal_rdkit_descriptor**(input_file, output_file, smiles_column)\r\n**Description:** \r\nThis function calculates RDKit molecular descriptors for molecules specified in a CSV file (`input_file`) using SMILES strings from a specified column (`smiles_column`). The calculated descriptors are appended as additional columns to the original data and saved in a new CSV file named `<input_file_name>_rdkit_descriptor.csv`.\r\n\r\n**Parameters:**\r\n- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.\r\n- `output_file` (str): Path where the output CSV file will be saved (optional if using the default naming convention).\r\n- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.\r\n\r\n**Output:** \r\nThe output will be saved as a CSV file named `<input_file_name>_rdkit_descriptor.csv`.\r\n\r\n---\r\n\r\n### **cal_lipinski_descriptors**(file_path, smiles_column, verbose=False)\r\n**Description:** \r\nThis function calculates Lipinski descriptors for molecules specified in a CSV file (`file_path`) using SMILES strings from a specified column (`smiles_column`). It automatically saves the calculated descriptors to an output file named `<input_file_name>_lipinski_descriptors.csv`.\r\n\r\n**Parameters:**\r\n- `file_path` (str): Path to the input CSV file containing molecular data in SMILES format.\r\n- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.\r\n- `verbose` (bool, optional): If `True`, the function will print additional processing details. Default is `False`.\r\n\r\n**Output:** \r\nThe output will be saved as a CSV file named `<input_file_name>_lipinski_descriptors.csv`.\r\n\r\n---\r\n\r\n### **cal_morgan_fpts**(input_file, smiles_column)\r\n**Description:** \r\nCalculates Morgan fingerprints for molecules specified in a CSV file (`input_file`) using SMILES strings from a specified column (`smiles_column`). The function saves the calculated fingerprints to an output file named `<input_file_name>_calculate_morgan_fpts.csv`.\r\n\r\n**Parameters:**\r\n- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.\r\n- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.\r\n\r\n**Output:** \r\nThe output will be saved as a CSV file named `<input_file_name>_calculate_morgan_fpts.csv`.\r\n\r\n---\r\n\r\n### **cal_mordred_descriptors**(input_file, smiles_column)\r\n**Description:** \r\nComputes Mordred descriptors for molecules specified in a CSV file (`input_file`) using SMILES strings from a specified column (`smiles_column`). The function saves the computed descriptors to an output file named `<input_file_name>_mordred_descriptors.csv`.\r\n\r\n**Parameters:**\r\n- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.\r\n- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.\r\n\r\n**Output:** \r\nThe output will be saved as a CSV file named `<input_file_name>_mordred_descriptors.csv`.\r\n\r\n---\r\n\r\n### **calculate_selected_fingerprints**(input_file, smiles_column)\r\n**Description:** \r\nBefore using this function, execute the following code snippet to download and unzip the necessary files:\r\n\r\n```bash\r\n! wget https://github.com/dataprofessor/padel/raw/main/fingerprints_xml.zip\r\n! unzip fingerprints_xml.zip\r\n## Molecular Fingerprint Calculation\r\n\r\nThis function calculates 12 different types of molecular fingerprints:\r\n\r\n- `AtomPairs2DCount`\r\n- `AtomPairs2D`\r\n- `EState`\r\n- `CDKextended`\r\n- `CDK`\r\n- `CDKgraphonly`\r\n- `KlekotaRothCount`\r\n- `KlekotaRoth`\r\n- `MACCS`\r\n- `PubChem`\r\n- `SubstructureCount`\r\n- `Substructure`\r\n\r\nEach enhanced dataset with fingerprints is saved as separate CSV files, appended with the respective fingerprint type name.\r\n\r\n### Parameters:\r\n- `input_file` (str): Path to the input CSV file containing molecular data in SMILES format.\r\n- `smiles_column` (str): The name of the column in the input CSV file that contains the SMILES strings.\r\n\r\n### Output:\r\nEach fingerprint type will be saved as a separate CSV file with the respective fingerprint type name appended. \r\nFor example: `<input_file_name>_AtomPairs2DCount.csv`.\r\n\r\n---\r\n\r\n## **fps**(filename, smiles_column, fp_type)\r\n\r\n**Description:** \r\nThis function calculates a specified molecular fingerprint (`fp_type`) for each molecule in a CSV file. 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